{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a1a4e851703498c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-04T16:43:27.689788600Z",
     "start_time": "2024-04-04T16:43:27.495580600Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def fun(args):\n",
    "#     a,b,c,d=args\n",
    "#     v=lambda x: (a+x[0])/(b+x[1]) -c*x[0]+d*x[2]\n",
    "#     return v\n",
    "# fun([1,2,3,4])([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6386383a0168b309",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T10:34:29.332593Z",
     "start_time": "2024-04-11T10:34:28.546072100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x143dd775ed0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('data.csv', header=None)\n",
    "plt.figure(figsize=(5, 4))\n",
    "plt.scatter(df.iloc[:, 0], df.iloc[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b95a89a27529fb16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T10:24:36.162787500Z",
     "start_time": "2024-04-11T10:24:34.689728500Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x143dbd2b250>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import integrate\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def cal_v(rn, rg, a, h0, t0, x):\n",
    "    v = a * (1 - np.exp(-2 * rg * (x - t0) *\n",
    "                        integrate.quad(lambda u: 1 - np.exp(\n",
    "                            -np.pi / 3 * rn ** 4 / rg * (x - t0) ** 3 * (1 - u) ** 2 * (1 + 2 * u)), 0, 1)[0])) + h0\n",
    "    return v\n",
    "\n",
    "\n",
    "x_arr = np.linspace(0, 80, 1000)\n",
    "y_arr = []\n",
    "for x in x_arr:\n",
    "    y_arr.append(cal_v(0.1, 0.1, 100, 50, 0, x))\n",
    "plt.scatter(x_arr, y_arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc0bbd14975d12b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-12T12:55:09.073088100Z",
     "start_time": "2024-04-12T12:55:08.481789200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17194e131d0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from test import *\n",
    "from matplotlib import pyplot as plt\n",
    "args = [6.26800391e-01 , 2.10366003e-01 , 6.53474474e+01, 0,\n",
    "  7.15157343e-01 , 4.47376230e+00,  9.07998450e+00 , 6.79685492e+01,\n",
    " 0, -1.28366404e-01]\n",
    "arr = np.loadtxt('data.csv', delimiter=',')[:400]\n",
    "x_arr = arr[:, 0]\n",
    "y = arr[:, 1]\n",
    "y_pred = np.array(overlay_funcs(x_arr)(args)).sum(0)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(x_arr,y_pred,label='predicted')\n",
    "plt.scatter(x_arr,y,label='actual')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c786add2515520bd",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
